TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems
Abstract
:1. Introduction
- A data- and layout-aware Bloom filter (DLBF) mechanism for effectively handling object and file level data integrity verification with object-based big data transfer systems.
- For efficiently handling dataset level integrity verification, we developed a two-phase Bloom-filter (TPBF)-based end-to-end data integrity verification framework for optimizing the memory and storage footprint when compared with state-of-the-art data integrity solutions.
- We utilized a Lustre file system [20,21,22] that interacts over an InfiniBand (IB) network [23,24] to evaluate the proposed design. Based on the experimental results, we can conclude that the proposed data integrity framework is very effective at detecting and resolving data integrity issues at all of object, file, and dataset levels.
- Data transfer performance, memory, and storage overhead have been evaluated to assess the overhead of the proposed end-to-end integrity verification framework in the context of data transmission. The experimental findings show that the suggested framework had 5% and 10% overhead on the total data transmission rate and on the total memory usage, respectively. Moreover, we observed significant ≥50% savings in terms of storage requirements, when compared with state-of-the-art solutions.
- The false-positive error rate was evaluated to assess the effectiveness of the proposed data integrity framework by manually inducing faults after transferring 20%, 40%, 60%, and 80% of the total data. Our experimental results showed that the proposed framework significantly reduced false-positive errors and was up to 80% more effective than current state-of-the-art solutions.
2. Background and Motivation
2.1. Background
2.1.1. Object-Based Big Data Transfer Systems
2.1.2. End-to-End Data Integrity
2.1.3. Big Data Transfer Frameworks
2.2. Motivation
- How can the impact of the data integrity framework on the overall data transfer rate of object-based big data transfer systems be minimized?
- How can the memory and storage requirements of the data integrity framework be reduced?
3. Related Work
4. Data Integrity Verification Framework
4.1. Bloom Filter Design
4.1.1. Bloom Filter Data Structure
4.1.2. Hash Optimization
- i =
- m = Bloom filter size
4.1.3. Data- and Layout-Aware Bloom Filter (DLBF)
- Insert: For each object ∈S, compute and set .
- Query: To check whether an object, , is in S, compute . If , the answer is yes; otherwise, the answer is no. However, if ,…, in the bit vector B are set to 1 as a result of hash collisions, then it results in false-positive errors.
- Insert: For each object ∈S, compute , and set , and also set the object layout information bit, = 1. Where, ‘i’ represents the layout of the object.
- Query: To check whether an object, , is in S, compute . If and = 1, the answer is yes; otherwise, the answer is no.
4.1.4. Illustration of Data and Layout Aware Bloom Filter
- Insert: The insert operation is shown in Figure 3a. To uniquely represent the object, the SHA-1 engine is employed to calculate the block hash on the dataset. In this illustrative example, objects A, B, and C are inserted into the Bloom filter. Hash functions {, , and } are employed on the hashed object data to uniformly map the objects into k random positions. The Bloom filter bits at positions {13, 16, and 20} are set to 1 using the {, , and } hash functions on hashed data. Additionally, bit {0} of the Bloom filter array is set to 1 as the layout of is zero. Similarly, bits at positions {1, 8, 28 32}, and {2, 20, 24, 26} are set to 1 for and , respectively.
- Query: The query operation is shown in Figure 3b. We considered objects C, D, and E for membership query operation. We presume the object membership if all the k bits in the Bloom filter section, along with the layout bit in the layout sections, are set to 1. For the Bloom filter returns “Positive” for membership query as the hash positions {20, 24, and 26} along with its layout bit at position {2} is set to 1. The membership query of returns “Negative” as the bit at position {11} is not set. On the other hand, membership query results in “Negative”, despite the fact that the bits at positions {8, 28, and 32} are all set. This is due to the fact that the object layout bit at position {4} is not set. Without the layout information, membership query may result in “False Positive” since the bits at positions {8, 28, and 32} are all set. Hence, we prevented false-positive matches of the Bloom filter by utilizing the object layout information in conjunction with the Bloom filter.
4.2. System Architecture
4.3. Design and Implementation
4.3.1. Communication Protocol
- The source endpoint sends a CONNECT request to the sink endpoint, and the sink endpoint responds with SUCCESS if the connection is successful.
- The source endpoint compiles a list of files to be transferred and then issues a NEW_FILE request for each file. The sink endpoint opens the file based on the information in the NEW_FILE request and adds the file descriptor to the FILE_ID response.
- The source endpoint schedules all the objects of a file and initiates object transfer using NEW_OBJECT request. The sink endpoint receives the object data and writes the same to the sink-end PFS. On successful write operation, the sink endpoint compares the block hash with the hash received in the NEW_OBJECT request and responds with OBJECT_SYNC.
- On successful integrity verification, both source and sink endpoints aggregate the file-based data and the layout-aware Bloom filter; otherwise, the source endpoint schedules the object for re-transfer.
- Steps 3 and 4 are repeated for all the objects in the file.
- On transferring all the objects of a file successfully, the sink endpoint compares the file hash with the hash received in the last object’s NEW_OBJECT request and responds with FILE_CLOSE response.
- On successful integrity verification, both source and sink endpoints aggregate the dataset level two phase Bloom filter; otherwise, the source endpoint schedules the file for re-transfer.
- Steps 2 to 7 are repeated for all the files in the dataset.
- After successfully transferring all of the files in the dataset, dataset level integrity verification is performed. If the integrity check is successful, the source endpoint will send a DISCONNECT request; otherwise, steps 2 to 9 will be repeated.
Listing 1. Communication message type. | |
typedef enum msg_type { | |
CONNECT = 0, | //Connection Request |
SUCCESS, | //Connection accepted |
NEW_FILE, | //New File request |
FILE_ID, | //Sink File ID. |
NEW_OBJECT, | //Ready for object transfer |
OBJECT_SYNC, | //Sync with Sink PFS |
FILE_CLOSE, | //File close |
DISCONNECT | //Ready to disconnect |
} msg_type_t; |
4.3.2. Data- and Layout-Aware Bloom Filter (DLBF)
Algorithm 1 Two-phase Bloom Filter | |
1: procedure GenerateDLBF((S)) | |
2: for each do | ▹ For all objects in a file |
3: | ▹ Map object of arbitrary size to fixed size |
4: for each to do | ▹ k hash functions |
5: | ▹ Calculate k hash bit positions |
6: | ▹ Set k hash positions of DLBF to 1 |
7: end for | |
8: | ▹ Set the layout bit of DLBF to 1 |
9: end for | |
10: | ▹ File signature |
11: return | |
12: end procedure | |
13: procedure GenerateTPBF((N)) | |
14: for each do | ▹ For all files in a dataset |
15: | ▹ Generate file level DLBF |
16: for each to do | ▹ k hash functions |
17: | ▹ Calculate k hash bit positions |
18: | ▹ Set k hash positions of TPBF to 1 |
19: end for | |
20: end for | |
21: | ▹ Dataset signature |
22: return | |
23: end procedure |
4.3.3. Two-Phase Bloom Filter (TPBF)
4.4. Memory Overhead Analysis
- C = number of active file transfers
5. Evaluation
5.1. Testbed and Workload Specifications
5.1.1. Testbed
5.1.2. Workload
5.1.3. Bloom Filter Configuration
5.2. Performance Evaluation
5.2.1. Data Transfer Time
5.2.2. Computational Overhead
- = Estimated data integrity overhead
- = TPBF average runtime
- = Standalone average runtime
5.2.3. Memory Overhead
5.2.4. Storage Overhead
5.2.5. False-Positive Matches
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Data Transfer Tool | Network Integrity | End-to-End Integrity | ||
---|---|---|---|---|
Object | File | Dataset | ||
Grid FTP | Yes | No | Yes | No |
BBCP | Yes | No | No | No |
XDD | No | No | No | No |
LADS | No | No | No | No |
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Kasu, P.; Hamandawana, P.; Chung, T.-S. TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems. Mathematics 2022, 10, 1591. https://doi.org/10.3390/math10091591
Kasu P, Hamandawana P, Chung T-S. TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems. Mathematics. 2022; 10(9):1591. https://doi.org/10.3390/math10091591
Chicago/Turabian StyleKasu, Preethika, Prince Hamandawana, and Tae-Sun Chung. 2022. "TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems" Mathematics 10, no. 9: 1591. https://doi.org/10.3390/math10091591
APA StyleKasu, P., Hamandawana, P., & Chung, T. -S. (2022). TPBF: Two-Phase Bloom-Filter-Based End-to-End Data Integrity Verification Framework for Object-Based Big Data Transfer Systems. Mathematics, 10(9), 1591. https://doi.org/10.3390/math10091591